According to the limit of resources in the subject of prioritization, one of the alternative methods is MCDM method. Generally, MCDM models have been developed under certainty while we confront with under uncertainty in real world. In hierarchical MCDM methods, one of the main steps is to weigh criteria and computes each alternative weight using defined criteria in the next steps. One of the easiest and most common weighting criteria methods is to apply the comparison matrices. The main approach in this paper is use of interval comparison matrices which is more realistic than classic methods.
In this paper, two MCDM models are provided respectively lexicographic goal programming (LGP) and two-stage logarithmic goal programming methods (TLGP) and used to prioritize investment plans. Such models are hierarchical methods developed in under uncertainty. At the end of this paper, a numerical example solved for each method and the results are compared with analytical hierarchy process (AHP) under certainty.